!Disclaimer! This package is based on the work of alshedivat/DeterminantalPointProcesses.jl and aims at keeping this package alive.
An efficient implementation of Determinantal Point Processes (DPP) in Julia.
- Exact sampling  from DPP and k-DPP (can be executed in parallel).
- MCMC sampling  from DPP and k-DPP (parallelization will be added).
logpmfevaluation functions  for DPP and k-DPP.
- Exact sampling using dual representation .
- Better integration with MCMC frameworks in Julia (such as Lora.jl).
- Fitting DPP and k-DPP models to data [3, 4].
- Reduced rank DPP and k-DPP.
- Kronecker Determinantal Point Processes .
Currently, no timeline, no milestones, no promisses.
Contributions are sought (especially if you are an author of a related paper). Bug reports are welcome.
 Kulesza, A., and B. Taskar. Determinantal point processes for machine learning. arXiv:1207.6083, 2012.
 Kang, B. Fast determinantal point process sampling with application to clustering. NIPS, 2013.
 Gillenwater, J., A. Kulesza, E. Fox, and B. Taskar. Expectation-Maximization for learning Determinantal Point Processes. NIPS, 2014.
 Mariet, Z., and S. Sra. Fixed-point algorithms for learning determinantal point processes. NIPS, 2015.
 Mariet, Z., and S. Sra. Kronecker Determinantal Point Processes. arXiv:1605.08374, 2016.